# [Run a local LLM chatbot on a Raspberry Pi 5](https://learn.arm.com/learning-paths/embedded-and-microcontrollers/llama-python-cpu/)

## In this learning path

- [Introduction](https://learn.arm.com/learning-paths/embedded-and-microcontrollers/llama-python-cpu/)
- [Run a Large Language Model (LLM) chatbot on a Raspberry Pi 5](https://learn.arm.com/learning-paths/embedded-and-microcontrollers/llama-python-cpu/llama-python-chatbot/)
- [Next Steps](https://learn.arm.com/learning-paths/embedded-and-microcontrollers/llama-python-cpu/_next-steps/)

## About this Learning Path

| Skill level: | Introductory |
|--------------|--------------|
| Reading time: | 30 min       |
| Last updated: | 13 Aug 2026  |

| Author: | Jason Andrews, Arm [GitHub](https://github.com/jasonrandrews) [LinkedIn](https://linkedin.com/in/jason-andrews-7b05a8) |
|----------|----------|
| Arm IP: | [Cortex-A](https://support.arm.com/?tab=compute-ip&Product%20Type=Application%20Processors) |
| Tags:  | [ML](https://learn.arm.com/tag/ml), [Linux](https://learn.arm.com/tag/linux), [LLM](https://learn.arm.com/tag/llm), [Generative AI](https://learn.arm.com/tag/generative-ai), [Raspberry Pi](https://learn.arm.com/tag/raspberry-pi), [Python](https://learn.arm.com/tag/python), [Hugging Face](https://learn.arm.com/tag/hugging-face) |

### Who is this for?
This is an introductory topic for anyone interested in running a local Large Language Model on a Raspberry Pi 5.

### What will you learn?
Upon completion of this Learning Path, you will be able to:
- Install the Python version of llama.cpp on your Raspberry Pi 5.
- Download an LLM from Hugging Face.
- Assess LLM memory size and performance.
- Run the LLM on your Raspberry Pi 5 using Python bindings for llama.cpp.

### Prerequisites
Before starting, you will need the following:
- A Raspberry Pi 5 running Raspberry Pi OS.

### Summary
You’ll run a local chatbot on a Raspberry Pi 5 with 64-bit Raspberry Pi OS. First, you’ll install the Python bindings for `llama.cpp`, choose a model that fits available memory, and download it from Hugging Face. Then, you’ll load the model, generate responses, and verify interactive inference directly on the Arm Linux device.

### Frequently asked questions

<details>
<summary>Which Raspberry Pi 5 model should I use for this chatbot?</summary>
The 8 GB RAM Raspberry Pi 5 model is preferred for exploring an LLM. Smaller memory configurations might restrict the model that you can run.
</details>

<details>
<summary>How do I know my Raspberry Pi OS installation is 64-bit?</summary>
Check your system information to confirm the operating system and kernel report a 64-bit build. If not, reinstall Raspberry Pi OS using the 64-bit image.
</details>

<details>
<summary>Can I follow these steps on another Arm Linux computer?</summary>
Yes. The instructions apply to other Arm Linux systems with minor modifications where Raspberry Pi–specific steps appear.
</details>

<details>
<summary>How do I choose a Hugging Face model that will run on my Pi?</summary>
Compare the model’s size to your available memory and leave headroom for the operating system and Python runtime. Select a model that fits comfortably within your RAM, then assess performance.
</details>

<details>
<summary>What result should I expect when I run the chatbot?</summary>
A Python script loads the chosen model and generates text responses in your terminal. If it starts and responds to prompts, your setup is working.
</details>